Processing of natural language to perform tasks or processes has become a valuable aspect of many devices. For example, processing a natural language input such as “call John Doe” to place a telephone call. However, traditional methods for processing natural language to perform tasks require that a developer input the possible language patterns before any processing can be performed, and/or they require inducing such patterns from past input. Unfortunately, the number of patterns of language input is for many tasks too great to be feasibly entered, even by very large teams of developers. Likewise, even enormous amounts of past input are often likely not sufficient for computational systems to determine how to appropriately deal with new inputs. Accordingly, traditional methods result in computational systems that understand extremely narrow, brief, and/or limited language, often with poor levels of precision.